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Educational Psychologist
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Luria's Theory of Brain Functioning: A Model for

Research in Cognitive Psychophysiology
Marlin L. Languis & Daniel C. Miller
Published online: 08 Jun 2010.

To cite this article: Marlin L. Languis & Daniel C. Miller (1992) Luria's Theory of Brain Functioning: A Model for Research in
Cognitive Psychophysiology, Educational Psychologist, 27:4, 493-511, DOI: 10.1207/s15326985ep2704_6

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 EDUCATIONAL PSYCHOLOGIST, 27(4), 493-51 1
Copyright Q 1992, Lawrence Erlbaum Associates, Inc.

Luria's Theory of Brain Functioning:

A Model for Research in
Cognitive Psychophysiology

Marlin L. Languis
Excellence in Learning, Inc.
Ohio State University
Daniel C . Miller
Texas Woman's University
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Luria's (1973) brain functioning theory is offered as a model for cognitive

psychophysiological research. It may be used to predict brain processing
patterns both for constructive tasks of various levels of complexity and for
high and low performance on these tasks. Brain mapping research is reported
that shows how brain processing patterns for tasks of varying cornp1exit:y are
associated with Luria's theory. As a model for research, Luria's model offers
several potential contributions to educational psychology. First, it may help
bridge the current gap between mainstream educational psychology and
cognitive psychophysiology. Second, it may be used to associate student
performance on cognitive tasks with brain processing patterns. For example,
differences have been demonstrated between high and low task performance
and brain processing patterns. Finally, the model proposed is potentially very
useful because it is empirically testable. The weight of the evidence to date
clearly supports the proposition that brain processing patterns and perfor-
mance in higher order, constructive cognitive tasks are related in a consistent
and predictable manner to Luria's brain functioning theory.

Many believe that cognitive science will guide education into the 21st
century. However, before substantial change can occur in education, the
wide gap between cognitive science and classroom practice must be closed.

Requests for reprints should be sent to Marlin L. Languis, Excellence in Learning, Inc., 4900
Read Road, Suite 202, Columbus, OH 43220-3164.
 494 LANGUIS AND MILLER

Is there a valid theory to bring the currently disparate fields of cognitive

science and education closer together?
The constructivist model, which maintains that individuals actively create
meaning as they process information, presents a dynamic alternative to
traditional static models of cognition (B. R. Dunn, 1975; Iran-Nejad &
Ortony, 1984; Wittrock, 1974). Thus, it seems reasonable to hypothesize
that individual differences in brain functioning may exist between those
with more and less efficient constructive processes. But what might these
differences be and how might they be detected in the brain? A general
theory along with recent advances in brain mapping technology can be used
to address this question. This article examines Luria's (1973) theory of brain
functioning from a psychophysiological perspective. We show how the
cognitive processes described in Luria's three functional units are reflected
in topographic brain mapping measures taken while subjects perform
cognitive construction tasks. We also show that differences exist, along the
lines suggested by Luria's theory, between the brain processing patterns of
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subjects classified as high and low performers on complex construction

tasks.
Topographic brain mapping researchers (Cobbs, 1986; Drake, Simmons,
& Languis, 1989; Duffy, Denkla, Bartels, Sandini, & Kiessling, 1980; Flynn
& Deering, 1989; Languis, 1985, 1988; Languis & Wittrock, 1986; Naour,
Languis, & Martin, 1991; Shucard, McGee, Cummins, Minarick, & Hill,
1991) have shown consistent differences in brain electrical activity patterns
during the performance of cognitive tasks. Children and adults with
learning difficulties were compared with normal peers. Differences in brain
functioning patterns have also been reported between high achieving,
academically gifted learners and their peers (Languis, Bireley, Brigner, &
Holland, 1990). Luria's theory offers a general model for relating construc-
tive brain functioning, topographic brain mapping, and efficiency in
performing complex tasks.

LURIA'S THEORY

Based on extensive clinical research, Luria (1973) proposed that the brain
has three functional units, "whose participation is necessary for any type of
mental activity" (p. 43). Luria identified these functional units as (a) Unit
One: the unit for regulating tone and waking and mental states (hereafter,
referred to as the arousal and attention unit); (b) Unit Two: the unit for
receiving, analyzing, and storing information (hereafter, the sensory input
and integration unit); and (c) Unit Three: the unit for programming,
regulation, and verification of activity (hereafter, the executive planning
and organization unit). Although assuming that each unit is associated with
 THEORY AND MAPPING 495

certain brain structures, Luria cautioned researchers against applying a

strict brain localization viewpoint to his theory. He stated (1980), "we
therefore suggest that the material basis of higher nervous system ]processes
is the brain as a whole, but that the brain is a highly differentiated system
whose parts are responsible for different aspects of the unified whole" (p.
35). The essence of Luria's theory is his explanation of how cognitive
processes are functionally organized. Therefore, it is more productive to
interpret his model as structure following function rather than as function
following structure.

The Arousal and Attention Unit

This functional unit involves activity in the brainstem including the reticular
formation, midbrain, pons, and medulla oblongata and is respon~siblefor
cortical tone (arousal) and selective attention. The unit is the basis of all
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human mental processes because it maintains a proper state of cortical tone

to allow the individual t o "tune into" experiences and initiate the selective
focus of attention. Without adequate functio~iingof this unit, cognitive
activities in the other two functional units would be impaired.

The Sensory Input and Integration Unit

This functional unit is composed of the sensory and the association areas.
The unit is ideally represented at the intersection of the temporal, occipital[,
and parietal lobes. In the normally lateralized adult, this region corresponds
roughly to Wernicke's area in the left hemisphere and to the: right
hemisphere analog. This unit is responsible for receiving, encoding, and
sorting information from the external world (Lashley, 1964).
Luria (1966) identified "two basic forms of integrative activity of the
cerebral cortex, by which different aspects of the outside world may be
reflected" (p. 74): simultaneous and successive. Simultaneous brain a.ctivity
involves an immediate apprehension and integration of various eleinents of
experience. The totality of experience is grasped all at once, and thus it
often is characterized as having spatial features. Successive processing
involves the sequential integration of stimuli into an organized temporal or
serial order. The mental coding of experience in these two ways is
fundamental to understanding the role of Luria's second functional unit in
cognitive processing.
According to Luria (1966), both successive and simultaneous coiding are
important, and each contributes a different component to language com-
prehension. Successive coding is evident in understanding the syntax of a
sentence because such coding involves the appreciation of the serial relation
 of one word to the next. Simultaneous processing is apparent in meaning
construction or its apprehension in a spatial configuration.
It is important to note that Luria's theory does not place successive and
simultaneous processing into different brain hemispheres. Nor should
specific tasks be considered as purely simultaneous or successive in nature.
Both hemispheres participate in both kinds of processing, and all tasks
contain elements of both. However, the differences that the two coding
processes represent may be evident in a student's learning style (Keefe,
Monk, Letteri, Languis, & R. Dunn, 1986; Languis, Sanders, & Tipps,
1980). Thus, a student may characteristically approach tasks with primary
emphasis on successive or simultaneous processing.
The relationship of successive and simultaneous processes is illustrated
well in a test done by the late Norman Geschwind on Perry Ward, who
suffered a stroke damaging the language area in the left hemisphere (Gilling
& Brightwell, 1982). When Geschwind showed Ward a card with the word
tree on it, Ward expressed the word orchard. Geschwind interpreted this
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phenomenon as being similar to deep dyslexia, in which the more specific

successive processing of language is impaired, and instead performance is
mediated more contextually by the brain's simultaneous processes.

The Executive Planning and Organizing Unit

This functional unit is composed of association cortex located in the frontal

and prefrontal areas of the brain, anterior to the sensory-motor strip. The
unit, often called the executor of the brain, is responsible for such activities
as impulse control, regulation of voluntary actions, and such linguistic
functions as spontaneous speech (Luria, 1980). Das (1980) suggested that
the third functional unit is the essence of human intelligence because it
involves the capacity for intentions, plans, asking new questions, solving
problems, and self-monitoring.

Relationships Among the Functional Units

Although the three units have distinct functions, they always work in
concert. For example, conscious mental activity takes place through the
combined action of all three units, the first providing the necessary cortical
tone, the second carrying out the analysis and synthesis of the incoming
information, and the third interacting, regulating, and verifying the con-
scious activity.
Two metaphors illustrate the interaction and interdependence of Luria's
functional units. The first metaphor is a basketball team. Each player has
a role and responsibility (e.g., guard, point guard, forward, strong forward,
center). Yet each player functioning in the assigned role individually does
 not result in a team. Effective team function results only when the defined
actions of each player are executed in an organized fashion with the skills of
the others. The second metaphor is an orchestra. Individual instruments
each play a role, but only when they are integrated does Beethoven's Fifth
Symphony result. Similarly, Luria's three units are orchestrated in cognitive
brain functioning.
Dynamic interaction of the units is also illustrated in the developrnent of
the attentional mechanisms of the brain. During early childhood, the ability
to attend to a task is primarily controlled by the arousal unit (reticular
activation system) of the brain. The prefrontal lobes become more fully
functional during puberty and assume more dominance over the arousal
unit of the brain. From that point on, the responsibility for regulating
attention and arousal, previously assumed by subcortical regions within the
first functional unit, is shared with prefrontal lobes.
Finally, consider the relationships between the prefrontal cortex and the
limbic system. The structures of the limbic system play primary roles in
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basic drives and expression of emotions and motivations such as eating,

fighting, fleeing, and mating. However, major cortical pathways exist
between the third functional unit and the limbic system, suggesting that
many motivational impulses originating in the midbrain are modulated by
the prefrontal cortex. (See B. Dunn, D. Dunn, Andrews, & Languis, 1992.)
Once again, one can see how planning, organization, and intentionality -
attributed to the third functional unit - are really the results of interaction
of all functioning units of the brain.
Damasio, Tranel, and Darnasio (1990) proposed a model of memory
which may have much in common with Luria's brain functioning theory,
especially in the way the levels of the brain interact. He argues for
hierarchical brain functioning in creating memories. At the bottom level of
the hierarchy is the sensory register. In the middle is a convergence zone,
and a more specific integrative zone exists at the top level. The convergence
zone actively links generic features with the raw data of the sensory register
and feeds it to the top layer where the most specific information processing
integrates information and generates the functional meaning from e:xperi.-
ence. Damasio et al.'s model of memory seems similar, in a general sense,
to Luria's model of brain functioning.
Finally, the close interaction among Luria's functional units is also
reflected in recent applications of Luria's theory to the field of
psychoeducational assessment (Naglieri & Das, 1990). In an early fine of
research, Das and colleagues (Das, Kirby, & Jarman, 1975) developed tasks
designed to measure simultaneous and successive processing. More recently,
Das, Naglieri, and their colleagues (Das, 1980; Naglieri & Das, 1988, 1990;
Naglieri, Prewett, & Bardos, 1989) have added planning and attention to
their program of research. This research has resulted in a four-factor model
 498 LANGUIS AND MILLER

based on Luria's theory. This planning, attention, successive processing,

and simultaneous processing model (PASS) is the basis of the Cognitive
Assessment System. The PASS system represents a major advancement in
intelligence testing and is particularly useful for those interested in applying
Luria's theory.
Having discussed Luria's theory, its three functional units, and their
closely interactive nature, we now examine it as a theory for research in
cognitive psychophysiology. We review the research using constructive
brain mapping tasks of varying complexity and discuss their relationships
with each of Luria's functional units and their interaction.

TOPOGRAPHIC BRAIN MAPPING

Topographic brain mapping was developed in the late 1970s by Frank Duffy
and his colleagues (Duffy, Burchfield, & Lombroso, 1979). During the first
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half of the 1980s, most of the literature related to brain mapping dealt with
basic sensory processing and very little higher cognitive processing. More
recently, researchers have began to use topographic brain mapping to focus
on the electrophysiological manifestations of higher cognitive functions
(Languis & Wittrock, 1986).
Topographic brain mapping begins with a computerized electroencephalo-
graphy (EEG) recording from the surface of the scalp. The brain electrical
activity is converted, moment by moment, to integrated color displays
called brain maps that show spatial, temporal, and electrical voltage
patterns. EEG brain wave activity reflects the summation of the mass action
of many neurons. Therefore, it is important to recognize at the outset that
topographic brain maps are a "brain in action" metaphor of the unified,
integrated brain at work. However, the fact that electrical activity is
maximal over a particular lobe of the brain does not guarantee that the
specific source of that activity is within the cortex immediately underlying
the scalp or even within that lobe of the brain. In fact, most studies have
indicated that multiple sources within the brain are responsible for a given
activity pattern. Theoretically, brain surface EEG may be best conceived of
as the summation of the joint activity in Luria's three functional units. In
fact, the very way that topographic maps are created, with "snapshot"
sequences of brain activity over time, very aptly represents the essence of
how Luria viewed brain functioning.
Topographic brain mapping holds great promise for measuring basic
cognitive processes such as those specified in Luria's model. Various
recording techniques and research methodologies have been employed to
measure these basic cognitive processes. One technique for evaluating
neurocognitive processes is to examine the frequency changes that occur
 THEORY AND MAPPING 499

during cognitive functioning. Another method is to record the EEG that is

time locked to some external stimulus, response, or condition. In the latter
method, a number of repeated trials is collected and subsequently average~d.
The background EEG is averaged out while unique electrical activity of the
brain related to the time-locked event becomes apparent. This time-locked
averaging technique produces what is known as an event-related potential
(ERP).
Final determination of exact ERP sources remains an open question at
this time. However, research using other brain imaging technology such as
magnetoencephalography, positive emission tomography scanning, and
depth-electrode recording can more precisely locate ERP sources. Torelllo
(1990) developed methods to superimpose topographic brain maps (brain
function) on magnetic resonance imaging (MRI) scans (brain structure). His
innovative work effectively demonstrated the value of integrating multiple
brain imaging technologies in appreciating the relationships and colntribu-
tions of each brain imaging system. Mathematical modeling using dipole
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analysis can also approximate the source of brain electrical activity pat-
terns.
Research along all these lines is already in progress around the world and
may be expected to gradually resolve the source enigma. It should be noted
that many of these alternative means of imaging are very expensive and that
some involve invasive procedures. Topographic mapping is, by contrast,
noninvasive. Ebersole (1991) reported data using brain mapping, dipolle
analysis, and subdural and depth-electrode recording to identify the focal
sources of EEG spikes in the same population of epileptic patients. The
results indicated that brain mapping, by itself, identified the slource of
epileptic seizures very satisfactorily.

TOPOGRAPHIC MAPPING AND

LURIA'S FUNCTIONAL UNITS

The Arousal and Attention Unit

Luria (1966) noted that "the phenomenon of evoked potential can be used
not only to indicate a direct response to specific sensory stimulus, but also
to record objectively changes in the reception and analysis of infolrrnation
arising through the mobilization of active attention" (p. 267). Tlie brain
activity associiated with Luria's first functional unit can be measured during
performance on tasks that require selective, focused, and sustained cia 11oca-
tion of attentional resources. According to Luria, under these cor~ditions,
"the attraction of attention by active expectancy or complication of the task
 leads to an appreciable increase in amplitude of the evoked potential" (1966,
pp. 267-268).
Luria was describing a pattern of brain activity that occurs between 100
and 200 msec after the presentation of a stimulus that requires voluntary
selective attention. When many time-locked occurrences of such stimulus
events are measured in the brain and averaged, the background EEG is
averaged out, and a smaller but significant event-related potential (ERP)
waveform is elicited. The waveform is well established in the research
literature as an index of selective attention and the initiation of encoding
(KIorman, 1991; Mirsky, 1987; Stewart & Moley, 1983). For auditory tasks,
the peak of the selective attention waveform comes about 100 msec after the
event and is called an NlOO ERP. For visual tasks, the selective-attention
waveform peaks a little later and is often called a ~ 1 0 0 ERP.
' Virtually
every ERP task has a selective attention component.

Selective attention. The auditory event-related potential (AERP) task

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is an example of a brain mapping task that evaluates a cognitive discrimi-

nation level of learning and requires both selective and focused allocation of
attentional resources. In an AERP task, the subject must consistently detect
differences (e.g., in pitch) between frequent (e.g., low) and randomly
occurring infrequent (e.g., high) tones and consciously focus attention on
the infrequent tones that are assigned as the target stimuli. The NlOO is
elicited during the processing of both nontarget and target events. However,
the amplitude of the NlOO is greater for target stimuli than for nontarget
stimuli (Squires & 0110, 1986).
Multiple local sources of AERP waveform are associated with selective
attention. They have not been completely identified, but they originate
primarily in brainstem and midbrain structures associated with Luria's first
functional unit (Mirsky, 1987). Sources outside the first functional unit may
also contribute to the selective attention waveform. There are ascending and
descending neural pathways between the frontal lobe of the brain and the
reticular activation system in the midbrain. There is clear evidence that the
executive function of the frontal lobe of the brain is involved in directing
subcortical structures of the midbrain to open the "gate" to allow some
stimulus events to pass upward to cortical structures for cognitive pro-

'Brain waves and ERP waveforms have characteristic sinusoidal wave characteristics. The
letter N (e.g., in N100) refers to negative voltages (below the "0" baseline) of the ERP
waveform at a latency of 100 ms; the letter P (as in PI00 or P300) refers to the positive
electrical voltages (greater than the "0" baseline) as they are displayed by the computer. At the
sites from which brain electrical activity is collected, one segment of the ERP epoch (the
negative or positive component) may predominate, leading to the N or P label. Therefore,
negative and positive voltages in ERP waveforms do not have any inherent "good" or "bad"
value meaning.
 cessing or to close the "gate" to block other incoming stimuli. Thus, the
brain mapping of attentional processes may include activity associated with
ascending and descending fibers from subcortical to frontal regions of the
brain, suggesting that multiple sources contribute to the selective attention
ERP and that the multiple sources are functionally closely intlerrelated
(Klorman, 1991; Mirsky, 1987; Woods, 1990).

Cognitive discrimination. Another electrophysiological comlponeat

of the AERP tasks is the well-known P300 waveform. The P300 was one of
the first ERP components identified (Sutton, Braren, Zubin, & John, 1967)
and is probably the most widely researched (Donchin, 1982; Picton &
Hillyard, 1974). The P300 peak generally has a positive, symmetric focus
over the central or parietal area of the brain at approximately 300 msec after
the onset of the target stimulus. A unique feature of the P300 is that it is
normally elicited for target but not for nontarget stimuli (in effective
learners). This ERP is elicited when the subject successfully discriminates a
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cognitively relevant event from an irrelevant one. However, in the absence

of the capacity to discriminate, P300 is generated by both target and
nontarget stimuli. For example, students with an auditory atterttional
disorder (ADD) often do not have capacity to discriminate and classify
target and nontarget tones discretely. ADD students generate a P300
waveform in response to both nontarget and target tones. This yields a
useful diagnostic brain mapping market for distractibility, Thus, the P300
component of the AERP is clearly tied to focused and sustained compo-
nents of attention (Languis, 1988; Languis & Wittrock, 1986).
Another line of ERP research has investigated the construct of limited
attentional resources in the brain. Results of attentional workload studies
have consistently shown that amplitude of the P300 decreases when the
learner is asked to do a second task concurrently. In addition, as the
difficulty of multiple tasks increases, the latency of the P300 also increases
(Hoffman, 1990). Taken together, attentional workload research supports
the argument that P300 taps attentional processing.
However, to do the discrimination associated with the propagation of
P300, the student must not only pay attention to (Luria's arousal and
attention unit) but also encode and classify, the tones in short-term memory
(Luria's sensory input and integration unit). Research with depth electrodes
has demonstrated that a primary source of P300 is in the hippocampus,
which is understood to have a major role in memory encoding. Thus, the
attentional discrimination component of the P300 is also related to siirnulus
encoding and integration in Luria's second functional unit.

Attention in complex tasks. Continuous performance tasks (CPT;

Excellence in Learning, 1991b) require a subject to maintain sustained
 attention (or vigilance) while consistently monitoring a sequence of items to
detect random occurrences of a target stimulus. Stimulus sequences of
varying complexity (geometric forms, numbers, letters) are used. In the
familiar Stroop Task (Stroop, 1935) color words are displayed in colors that
match or mismatch. The target requirement is to respond to every match.
This task, therefore, is relatively complex because it requires continuous
cognitive evaluation. The brain mapping Stroop Task (Excellence in
Learning, 1991e), which includes the color word, is also pronounced so a
match or mismatch of three task elements must be evaluated.
In relatively complex CPT tasks, efficient learners (those with low error
rates) generate a symmetric ERP waveform that peaks over central and
parietal regions of the brain at about 500 msec post-stimulus. By contrast,
inefficient learners (with high error rates) produce a weaker, more diffuse,
and asymmetric ERP. These brain processing patterns require active
attention (Luria's first functional unit), but they also involve coding and
integration of task sequences into meaningful forms (Luria's second
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functional unit).

Attention and personality variables. In two studies, Wilson (1989)

and Wilson and Languis (1990a, 1990b) examined AERP patterns in 4
strongly introverted and 4 extraverted adults (N = 8) and in 16 extroverted
and 17 introverted male high school students (N = 33). Introverts and
extraverts were identified using the Myers-Briggs Type Indicator (MBTI;
Briggs & Briggs-Myers, 1943). Jungian theory, upon which the MBTI is
based, suggests a greater level of internal arousal in introverts and a seeking
of stimulation from the outside world in extraverts. Wilson and Languis
compared the P300 for target and nontarget tones. The predicted pattern
was found in both data sets. Introverts showed greater evoked potential
amplitudes than extraverts. The differences were statistically significant ( p
= < .05) across central and parietal brain areas.

Sensory Input and Integration Unit

We have already seen that coding and integration processes generate ERP
waveforms at about 300-500 msec post-stimulus in tasks with a strong
attentional component. We now turn more directly to successive and
simultaneous processing tasks characteristic of Luria's second functional
unit.

Working memory tasks. In the familiar Sternberg memory paradigm,

the student views a set of serially presented individual digits and is asked to
recall if a subsequent target digit was a member of the previous set. This
task involves holding the first set in working memory and scanning it to
 determine whether the target was a member of the display set. This taslk
clearly goes beyond the discrimination learning examined earlier in connec-
tion with Luria's first functional unit and involves the encoding and
integration processes of his second functional unit.
The most distinctive ERP produced in the brain for the Sternberg Task
(Excellence in Learning, 1991d) is a waveform that peaks over the parietal
region of the brain; this primary ERP is superimposed on a slow negative
potential commonly referred to as contingent negative variation (Halgren,
1990). Many related working memory tasks (e.g., encoding aincl later
recalling lists of words) have encoding and integration requirements similar
to the Sternberg task, and they all produce a waveform that is maximal over
the brain regions associated with Luria's second functional unit.

Strategic coding and integration. Working memory tasks clo not

require frequent mental shifts, recognition, or deployment of new plans
during the primary task of encoding and integration. However, if the
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learner is required to use encoding and integration tasks that necessitate

flexible strategic processing, a different and distinctive ERP complex
results. This is a negative ERP waveform that is maximal over the frontal
region of the brain within the first 300 msec followed by the movement of
the processing negativity to the parietal lobe of the brain during the next 300
msec. This ERP complex occurs in tasks requiring the flexible executive and
planning strategies characteristic of Luria's third functional unit, a s docu-
mented in the Miller's (1989a) study of the Category Test discussed later in
the section on Luria's third functional unit.

Semantic coding. Semantic coding tasks also require primary use of

successive or simultaneous integration processes of Luria's secon~dfunc-
tional unit, with possible involvement of executive and planning strategies
of his third functional unit. Kutas and Hillyard (1980) have replorted
electrophysiological data in this area. They discovered an ERP peaking
approximately 400-600 msec after stimulus onset over parietal brain areas.
The N400 was elicited when semantically incongruent words were presented
at the ends of otherwise meaningful sentences. The N400 is a fairly robust
negative potential and is characterized by a parietal distribution (Kutas &
Hillyard, 1980). Extensive work with the N400 waveform has suggested that
its onset may be related to violations of semantic expectancies during
language processing (D. A. Dunn, 1988).

Spatial visualization task. Luria's second functional unit is the: center

for the processing of spatial information. The right parietal area of the
brain is well established as a center for the encoding and integration of
spatial information (Benton, 1956). In addition, complex spatial visualiza-
 tion tasks may require active mental constructions and manipulations of
spatial task components, implicating the involvement of Luria's third
functional unit. Consider a learner who mentally visualizes a two-
dimensional representation of the hidden side of a three-dimensional
object. The learner must code and integrate the three-dimensional object,
but active mental construction and strategic thinking are also required.
The Spatial Visualization Test (SVT; Excellence in Learning, 1991~)is a
computerized measure of spatial visualization. It was designed by Bertoline
and Miller in 1990. Languis (1990) and Languis, Miller, and Bertoline
(1990) reported item difficulty and test-retest validation of the SVT.
Isomorphic projections of three-dimensional geometric forms are dis-
played, and the learner is instructed to mentally visualize the object from a
prescribed perspective: top, bottom, front, back, right, or left. ERP data is
collected during this mental activity. The learner's accuracy in visualizing
the prescribed perspective is then assessed by asking the subject to select the
correct perspective from an array of all six perspectives. The SVT assesses
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both performance and reaction time item by item and gives immediate
(correct or incorrect) feedback to the student for each item.
Languis used the SVT in a brain mapping study of normal college adult
volunteers (N = 75). Subjects were classified into high and low spatial
visualizers based upon the number of errors they made on the SVT test.
There were 17 high spatial visualizers (more than one standard deviation
below the group error mean) and 16 low visualizers (more than 1 SD above
the group error mean). High visualizers produced an ERP waveform
pattern first over the frontal area of the brain and subsequently over the
parietal area of the brain (see Figure 1). This pattern reflects, in addition to
the primary involvement of encoding and integration processes, the partic-
ipation of Luria's planning and organizing third functional unit. Low
visualizers did not display the processing pattern over either the frontal or
parietal brain areas. Nonparametric analysis of variance (ANOVA) statis-
tical evaluation (Kruskal-Wallis) revealed group differences @ > .03) for
peak ERP over midline frontal and parietal scalp sites. Finally, a Pearson
correlation was performed for all 75 subjects in the study, SVT errors and
ERP peak amplitudes at frontal and parietal regions were correlated ( p <
.05). At both scalp sites, as error scores decreased, ERP amplitudes
increased.

Executive Planning and Organizing Unit

Is planning, organizing, or making adaptive mental shifts required - or all

three? This question describes the criterion for selecting tasks involving
Luria's third functional unit. Recent brain mapping research in the Brain
 High Visualizers
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Lov Visualizers

FIGURE 1 Normative brain mapping patterns high and low visualizers.

 Behavior Laboratory at Ohio State University has focused on addressing
this question.

Cognitive flexibility. Cognitive flexibility requires a complex set of

processes that Halstead (1947) called abstraction ability. To measure this
ability, Halstead built the Category Test, one of the five components of a
neuropsychological battery. This test captures a wide range of abilities
(Golden, Osmon, Moses, & Berg, 1981; Reitan & Wolfson, 1986). It
appears that the Category Test taps several functions specific to various
types of brain processing. The ability to shift a cognitive set is a frontal-lobe
function (Walsh, 1978), and the ability to extract an element out of the
whole involves parietal activity (Hecaen & Albert, 1978).
In our lab, the Computer Version of the Category Test (CVCT; Excel-
lence in Learning, 1991a) was developed by Miller (1989b) to run on a
Macintosh computer. The CVCT was linked to the brain mapping computer
using a triggering program developed by Languis (1989). Miller collected
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brain mapping data for the CVCT in the raw EEG mode with embedded
pulse markers to identify the presentation of each task stimulus and each
student response. Using a counterbalanced design, Miller administered both
the brain mapping CVCT and the paper-and-pencil Category Test (Byrd,
1987) to 32 normal male high school volunteers. Miller also administered
the planning, attention, simultaneous and successive subtests at the DN:
CAS (Das-Naglieri: Cognition Assessment System, 1992) to the same
students.
There were three major findings. First, performance on the CVCT was
equivalent to that on the paper-and-pencil Category Test. Second, ERP
waveforms were propagated over the frontal and parietal regions of the
brain as predicted by Luria's theory. And, finally, performance on the DN:
CAS correlated positively with brain electrical activity patterns over frontal
and parietal lobes of the brain.

Levels of task performance. Languis (1990) proposed that differ-

ences in task efficiency might be investigated by comparing brain mapping
data of subjects performing complex tasks with few errors (high-efficiency
subjects) and with many errors (low-efficiencysubjects). One would expect
greater processing in Luria's third functional unit for complex tasks for
learners who are performing efficiently. In other words, those who process
tasks well would tend to employ effective learning strategies to do the tasks.
They would actively plan, organize, and use adaptive mental shifts to attack
the tasks. Further, they would be more metacognitively aware of the task
requirements and of their own thinking processes. One would also expect
efficient task performers to be confident self-monitors and self-regulators.
Brain research completed in our lab has shown differences in processing
 associated with task efficiency. We have discussed the difference in strength
of the ERP over central- and parietal-brain regions for students with low
and high error rates in complex CPT tasks. Similarly, efficiency in spatial
visualization was displayed in an ERP pattern first over frontal and then
over the parietal areas of the brain for high spatial visualizers but mot for
low visualizers. The weight of this evidence supports the integrateid action
of Luria's second and third functional units.
More research is in progress examining the predictions of Languis's
(1990) task-efficiency model. Miller (1991) is assessing brain rnapping
patterns generated during the performance of computerized versions of the
DN: CAS tasks and a computerized version of the Memory for Narnes Test
from the Woodcock-Johnson Psychoeducational Battery-Revised (\Yood.-
cock, Miller, & McCullough, 1991). Preliminary findings show the expected
differences in frontal- and parietal-lobe activity between high anld low
performers for planning and attention tasks. The visual-auditory associa-
tions required by the Memory for Names Test yields expected parietal,
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temporal, and occipital activation as predicted by the model.

IMPLICATIONS

At the beginning of this article, we identified a need to bridge the present

gap between contemporary brain research and current practice in education
and psychology. A general cognitive theory of brain functioning was
needed. We have shown that Luria's model is one such theory. We saw how
this theory can relate various levels of task complexity to brain functialning.
We further argued that Luria's theory might be used to predict differences
in brain functioning between students who perform cognitive tasks effi-
ciently and those who do not. Evidence to date supports the proposition
that brain processing patterns and performance in higher order coginitive
tasks are related in a manner consistent and predictable with Luria's theory
of brain functioning. Additional psychophysiological research should be
carried out to empirically test the model proposed in this article.
As the body of evidence grows relating levels of task performance and
brain-functioning theory, our diagnostic capability will be enhanced. Task
performance may be interpreted more precisely and with greater insight by
tying it to brain processing and neuropsychological theory.
Further, as the relationship between brain processing and task perfor-
mance is more fully established, educational interventions can be designed
and implemented from a stronger cognitive-science basis. Educational
interventions may take two forms: (a) changes made by the teacher in
curriculum or instruction to better match student cognitive processing
patterns and learning styles, or (b) changes made by the learner in impr~oved
 self-management and competence in cognitive skills. The learner may set
goals, build cognitive skills and learning strategies to help reach those goals,
and then monitor progress toward them.
The opportunity to monitor the effectiveness of teaching students to
employ higher level thinking skills by assessing changes in the brain is an
exciting challenge and a long-range goal. A beginning has been made along
these lines by pre- and posttesting students with brain mapping to evaluate
changes associated with directed cognitive interventions. The evidence of
improvement and normalization of brain processing patterns reported in
these case studies has been consistently encouraging (Andrews, 1986;
Languis & Wittrock, 1986).
Finally, the integration of Luria's brain-functioning theory, cognitive
brain mapping, and levels of task performance suggests an approach to
viewing learner differences more positively as modifiable variation in
cognitive processing rather than as unmodifiable learning disabilities or
deficits.
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